Increasing demand for renewable electricity generation resources is driven by a desire to mitigate the climate impact of fossil-based electricity generation and satisfy ever growing electric system load. However, each class of renewable generation comes with one or more disadvantages that limit the degree to which they can be integrated in bulk system operation. Hydro-electric generation has long been employed as a significant renewable source of electricity. But, climate change may jeopardize the magnitude and certainty with which the existing asset base can meet demand, while population displacement, habitat destruction and fish stock degradation limit the growth of new assets. Shifts in both load and hydro-electric generation potential increase uncertainty in long term planning and further enhance the need for technological configurations that support operational flexibility.
Meanwhile, wind power has seen rapid growth in recent years, but the need for reliability resources limits the penetration of wind generation without additional mitigation measures such as firming resources. Solar resources are also becoming increasingly available but have intermittency challenges similar to those of wind. In addition, residential rooftop solar resources are challenging the classical utility revenue model and are known to cause voltage control issues in distribution systems. Finally, the reliable, robust control and optimal operation of an increasingly complex bulk electricity system has become a very real concern.
The traditional utility approach to renewable intermittency is to allocate additional firm reliability resources to replace all potentially non-firm renewables resources. These firm resources are generally fast-responding thermal fossil resources and where possible hydro-electrical resources as well. For new renewable resources the impact of this approach is quantified as an intermittency factor, which discounts the contribution of wind in addition to its capacity factor and limits the degree to which they can contribute to meeting peak demand. However, the intermittency factor does not account for the ramping requirements created by potentially fast-changing renewable resources. The need for fast-ramping resources discourages the dispatch of high-efficiency fossil and nuclear generation assets while promoting low-efficiency fossil and hydro where available for regulation and reserve services.
Demand response is widely regarded as a low-cost alternative to fast-response generation reserves that reduces the dispatch of inefficient generation resources. But load control strategies for demand response applications can be challenging to deploy. This is in part because the competing objectives of local and global control. It is also in part because of the complexity of the models and the simplifications required to make their analysis and design analytically tractable, numerically feasible in simulations for large-scale adequacy, and realizable in renewable integration studies.
Effective and widely used strategies for optimizing the scheduling and operation of bulk-system resources use markets to solve the cost-minimizing resource-allocation problem since they are proposed in the early 1980s. Market-based control strategies were later adapted to building control systems, generalized to feeder-scale operations, then utility-scale operations, and most recently proposed for ancillary services. Models of varying complexity have been used to study the control of aggregate loads in these cases. However, stability concerns remain when prices are used to dispatch loads and the design of general utility-based generation-following load control systems either by direct command and control or by indirect market-based control remains a largely unresolved problem.
One conventional practice for direct load control employs so-called “one-shot” load shedding strategies for emergency peak load relief only. This approach uses a controllable subset of thermostatic loads in a particular class, e.g., water heaters or air-conditioners, which are transitioned to a curtailed regime that reduces the population average power demand. After a time, these responsive load are released and return to their normal operating regimes. This strategy exhibits fluctuations in load during the initial response as well as demand recovery rebounds after the loads are released. For these reasons, “one-shot” direct load control strategies are sometimes enhanced using multiple subgroups of the responsive loads dispatched in a sequence that smooths the overall response of the load control system. However, these strategies require some knowledge of the aggregate thermal response of the buildings in which the loads are operating. In addition, these strategies are not well-suited to the more general tracking problem where load “follows” intermittent generation and have a variety of shortcomings including saturation, high sensitivity to modeling errors and noise, and stability considerations due to delays.
Aggregate building thermal load models present additional challenges when thermostatic loads are being considered. A switched-mode representation of the individual building thermal response is used to account for hysteresis of thermostats, which in turn gives rise to high-order non-linear aggregate load models. Models also include so-called “refractory states” associated with state transition delays rather than thermal parameters due to deadband of the thermostats. Tractable state space models of aggregate loads rely on model-order reduction strategies that linearize the system model and limit the number of state variables used to represent responsive loads, as illustrated in FIG. 1. These state space models represent thermostats with non-zero deadband.
At least some embodiments are directed towards apparatus, systems, and associated methods for controlling thermostatic loads which overcome shortcomings of the conventional control strategies.